{"id":"W3048214116","doi":"10.1016/j.bbmt.2020.08.003","title":"A Personalized Prediction Model for Outcomes after Allogeneic Hematopoietic Cell Transplant in Patients with Myelodysplastic Syndromes","year":2020,"lang":"en","type":"article","venue":"Biology of Blood and Marrow Transplantation","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute of Allergy and Infectious Diseases; National Cancer Institute; Office of Naval Research; National Heart, Lung, and Blood Institute; Health Resources and Services Administration","keywords":"Medicine; Myelodysplastic syndromes; International Prognostic Scoring System; Oncology; Internal medicine; Cohort; Concordance; Transplantation; Hematopoietic stem cell transplantation; Myeloid; NPM1; Bone marrow; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007763153,0.0001531069,0.0003643174,0.0001089547,0.00002550402,0.000003291578,0.00004452186,0.0001325358,0.00001129045],"category_scores_gemma":[0.000009039362,0.0001134752,0.00005654981,0.00008438409,0.0001183969,0.00005445066,0.000003917986,0.0001070745,7.356836e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001255865,"about_ca_system_score_gemma":0.00008465226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008397545,"about_ca_topic_score_gemma":0.00001288425,"domain_scores_codex":[0.9990916,0.00003662418,0.0002661473,0.0002811344,0.0001195886,0.0002048963],"domain_scores_gemma":[0.9996277,0.00009315193,0.0000552367,0.00006543152,0.00005651794,0.0001019418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009101357,0.0002397984,0.9544491,0.00206631,0.0001655243,0.0000237867,0.003529014,0.00006898274,0.03016436,0.00005412794,0.000005713214,0.0001319136],"study_design_scores_gemma":[0.05611611,0.004958685,0.9112939,0.0004481194,0.002449776,0.00009035723,0.0001705622,0.0133255,0.0105996,0.0001277794,0.00001330585,0.0004063171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887691,0.0002023576,0.009165531,0.0004379071,0.00001570432,0.000809564,0.0005630226,0.00002208253,0.000014796],"genre_scores_gemma":[0.9969767,0.0002766209,0.001839053,0.0001840395,0.00001067867,0.0001226083,0.0005461844,0.00001786939,0.00002624405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04701475,"threshold_uncertainty_score":0.4627383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369413417340228,"score_gpt":0.238448414300949,"score_spread":0.2247542801275467,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}